Sequence Processing with Quantum Tensor Networks
For the first time, Quantinuum researchers have run scalable quantum natural language processing (QNLP) models, able to parse and process real-world data, on a quantum computer. In a recent paper, the researchers define machine learning models for the task of classifying sequences – which can be anything from sentences in natural language, like movie reviews, to bioinformatic strings, like DNA sequences. Classifying sequences of symbols – letters, words, or longer fragments of text – is an obviously useful computational task, and has led to some of the decade’s biggest changes; we now see this technology in use in everything from chatbots to legal cases.
Current classical models, which are based on neural networks, primarily look at the statistical distributions of where words are put with respect to each other – they don’t really consider the structure of language a priori (they could, but they don’t). In contrast, syntactic information scaffolds Quantinuum’s new quantum models, which are based on tensor networks, making them “syntax-aware”. Considering things like structure and syntax from the beginning allows scientists to create models with far fewer parameters, that require fewer gate operations to run, while allowing for interpretability thanks to the meaningful structure baked in from the start. Interpretability is the most pressing challenge in artificial intelligence (AI) — because if we don’t know why an algorithm has given an answer, we can’t trust it in critical applications, for instance in making medical decisions, or in scenarios where human lives are at stake.
Both neural and tensor networks can capture complex correlations in large data, but the way they do it is fundamentally different. In addition, since quantum theory inherently is described by tensor networks, using them to build quantum natural language processing models allows for the investigation of the potential that quantum processors can bring to natural language processing specifically, and artificial intelligence in general.
Thanks to best-in-class features like mid-circuit measurement and qubit reuse on Quantinuum’s H2-1 quantum processor, they were able to fit much larger circuits than one might naively expect. For example, the researchers were able to run a circuit that would normally take 64 qubits on only 11 qubits. Combined with the reduced number of gates required, these models are entirely feasible on current quantum hardware.
This paper shows us that we can run, train, and deploy QNLP models on present-day quantum computers. When compared to neural-network-based classifiers, the quantum model does just as well on this task in terms of prediction accuracy. What’s more, this work encourages the exploration of quantum language models, as sampling from quantum circuits of the types used in this work could require polynomially fewer resources than simulating them classically.
Kaniah is Chief Legal Counsel and SVP of Government Relations for Quantinuum. In her previous role, she served as General Counsel, Honeywell Quantum Solutions. Prior to Honeywell, she was General Counsel, Honeywell Federal Manufacturing and Technologies, LLC, and Senior Attorney, U.S. Department of Energy. She was Lead Counsel before the Civilian Board of Contract Appeals, the Merit Systems Protection Board, and the Equal Employment Opportunity Commission. Kaniah holds a J.D. from American University, Washington College of Law and B.A., International Relations and Spanish from the College of William and Mary.
Jeff Miller is Chief Information Officer for Quantinuum. In his previous role, he served as CIO for Honeywell Quantum Solutions and led a cross-functional team responsible for Information Technology, Cybersecurity, and Physical Security. For Honeywell, Jeff has held numerous management and executive roles in Information Technology, Security, Integrated Supply Chain and Program Management. Jeff holds a B.S., Computer Science, University of Arizona. He is a veteran of the U.S. Navy, attaining the rank of Commander.
Matthew Bohne is the Vice President & Chief Product Security Officer for Honeywell Corporation. He is a passionate cybersecurity leader and executive with a proven track record of building and leading cybersecurity organizations securing energy, industrial, buildings, nuclear, pharmaceutical, and consumer sectors. He is a sought-after expert with deep experience in DevSecOps, critical infrastructure, software engineering, secure SDLC, supply chain security, privacy, and risk management.
Todd Moore is the Global Vice President of Data Encryption Products at Thales. He is responsible for setting the business line and go to market strategies for an industry leading cybersecurity business. He routinely helps enterprises build solutions for a wide range of complex data security problems and use cases. Todd holds several management and technical degrees from the University of Virginia, Rochester Institute of Technology, Cornell University and Ithaca College. He is active in his community, loves to travel and spends much of his free time supporting his family in pursuing their various passions.
Retired U.S. Army Major General John Davis is the Vice President, Public Sector for Palo Alto Networks, where he is responsible for expanding cybersecurity initiatives and global policy for the international public sector and assisting governments around the world to prevent successful cyber breaches. Prior to joining Palo Alto Networks, John served as the Senior Military Advisor for Cyber to the Under Secretary of Defense for Policy and served as the Acting Deputy Assistant Secretary of Defense for Cyber Policy. Prior to this assignment, he served in multiple leadership positions in special operations, cyber, and information operations.